Cloud-native environments require observability best practices to help IT teams and businesses make their systems more efficient and proactively improve the end-user experience.
As cloud computing becomes more prevalent in creating and delivering services, it becomes clear that: Observability best practices It’s a concern for the entire business, not just the IT team.
Wait, what is observability?
Observability is the ability to identify the internal state of a system in real time based on information/data of the external state of the system.
Observability relies on the output generated throughout the system to identify the proverbial needle in the haystack. This enables multidisciplinary teams to quickly resolve issues before they impact the end-user experience.
This blog delves into a lot of different things. Observability best practices All organizations should implement. Let’s start with the basics!
As mentioned earlier, it’s a way to check the internal health of your system based on the many outputs generated across your multi-cloud environment.
If you can interpret these records to gain insight into the problem to its root cause, and then resolve the problem without additional coding or testing, the system is observable and ensures that the system is efficient. .
Observability has become a critical aspect of cloud-based services due to the increasing complexity of dynamic, uniquely distributed, cloud-native architectures.
However, in understanding observability, many people misunderstand it as a buzzword for network performance management (NPM) or application performance management (APM).
Essentially, monitoring allows you to pre-configure alerts for possible problems. Therefore, monitoring tools work on the assumption that you already know all the problems that may arise during the lifetime of your system.
However, cloud-based systems are dynamic. In other words, it’s almost impossible to know all possible issues in your pipeline with just APM or NPM.
On the other hand, if your system is fully observable, you will be alerted immediately if something goes wrong. This helps the cross-functional team understand the problem and fix it quickly.
moreover, Observability best practices Enterprises and DevOP teams can detect the severity of an issue and its impact on the entire pipeline to the end-user experience. Therefore, it helps in wise use of resources and time.
See also Observability, infrastructure complexity, shift left
Know your platform before approaching an observability tool vendor. Identifying all sources of data feeds is essential.
This is why auditing allows us to understand the requirements, such as at what level of the system we need observability and how broadly observability we need.
Cloud-native environments, as well as related development practices (continuous integration and continuous development (CI/CD), agile development, multiple programming languages, etc.) are becoming incredibly complex every day.
This makes it counter-intuitive (and resource-intensive) to observe possible system-wide failures.
Therefore, the first step, a thorough system audit, is essential to establish the necessary data feeds or outputs, and immediate attention should be paid to avoid impacting the overall system or impacting the user experience. have to pay
As an addendum to the previous Observability best practicesit is important not to set observability alerts for every error that occurs in the system.
Many issues flagged by such tools are relatively minor issues, such as system updates or patches, which the system administrator may have already fixed. Therefore, those alerts are counterproductive.
Instead, enable alerts for errors or problems that cannot be resolved by automated offerings. This frees DevOps teams to focus on more pressing issues, creating an efficient ecosystem.
Data logging across multi-cloud environments is essential for observability, providing greater insight into systems and possible errors. They pinpoint the root causes of errors in the system and how prevalent such problems are.
However, data logging is often ineffective, perhaps because there are not enough logs or there are too many logs that serve no purpose.
Too little or too much logging leads to loss of context or too much fuss. Hence observability efficiency is compromised. This can double the cost and effort.
Therefore, one of the observability best practices is to create a standardized data log format to filter data at multiple levels.
This way, irrelevant data can be avoided and only logs that provide information about critical issues such as unique user IDs and timestamps are saved.
However, the logging format also needs to be aggregated and centralized. Data that seems irrelevant to the operations team can be useful to cross-check with other data feeds.
This data format facilitates interdisciplinary collaboration and allows for more efficient data storage and system-wide insight.
The general idea is that to effectively implement observability in a system, only logs, metrics, and traces should be monitored.
However, such raw telemetry from a backend perspective can be misleading and provide a distorted picture of system performance.
So it’s equally important to consider the data feeds from the front-end application, how the system is working for real end users.
End-user experience information is critical from an outside-in perspective, eliminating potential blind spots and directly contributing to better business outcomes.
Building observability into existing instrumentation is important for enterprises, and open source tools make the job easier.
Open-source solutions provide pre-built standards for collecting data, improving observability in cloud-based environments.
This allows multidisciplinary teams to have a standardized understanding of the internal state of systems across multiple settings.
Organizations can also leverage real-time user monitoring to gain a deeper understanding of user experience and how every request interacts with various services along the pipeline.
This gives DevOps, SRE, and IT teams insight into the user request journey and overall system health.
You can then proactively fix new issues before they impact performance or user experience. It also makes it easier to recover from problem areas.
Observability is important in cloud-native applications because unpredictability is the name of the game.
However, the development team alone cannot achieve this.
To improve communication and collaboration between development and operations teams, the entire organization must adopt a DevOps culture.
Achieving this requires implementing end-to-end responsibility, lack of willingness and fear of failure, constant improvement and focus on customer needs, and maximum automation.
When these are achieved and everyone in the enterprise works towards a common goal, the system can have full observability, streamlined processes, and improved efficiency.
It can also prepare your team for unexpected problems.
Some problem areas, such as coding issues, cannot be solved by automation alone.
Therefore, it is imperative to integrate observability into trouble ticket submission and help desks so that problems are detected and the appropriate IT staff assigned to fix them.
Cloud computing and related agile development and CI/CD practices have greatly contributed to augmenting business services.
However, increasingly complex architectures make it much more difficult for SRE and DevOps teams to identify and resolve issues.
Therefore, cloud-native environments have best observability practice To help IT teams and businesses make their systems more efficient and proactively improve the end-user experience.
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